Accuracy of administrative claims data for polypectomy
Bibliographic record
Abstract
BACKGROUND: The frequency of polypectomy is an important indicator of quality assurance for population-based colorectal cancer screening programs. Although administrative databases of physician claims provide population-level data on the performance of polypectomy, the accuracy of the procedure codes has not been examined. We determined the level of agreement between physician claims for polypectomy and documentation of the procedure in endoscopy reports. METHODS: We conducted a retrospective cohort study involving patients aged 50-80 years who underwent colonoscopy at seven study sites in Montréal, Que., between January and March 2007. We obtained data on physician claims for polypectomy from the Régie de l'Assurance Maladie du Québec (RAMQ) database. We evaluated the accuracy of the RAMQ data against information in the endoscopy reports. RESULTS: We collected data on 689 patients who underwent colonoscopy during the study period. The sensitivity of physician claims for polypectomy in the administrative database was 84.7% (95% confidence interval [CI] 78.6%-89.4%), the specificity was 99.0% (95% CI 97.5%-99.6%), concordance was 95.1% (95% CI 93.1%-96.5%), and the kappa value was 0.87 (95% CI 0.83-0.91). INTERPRETATION: Despite providing a reasonably accurate estimate of the frequency of polypectomy, physician claims underestimated the number of procedures performed by more than 15%. Such differences could affect conclusions regarding quality assurance if used to evaluate population-based screening programs for colorectal cancer. Even when a high level of accuracy is anticipated, validating physician claims data from administrative databases is recommended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.193 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".